Lacuna Research MCP

🔬 Ground your coding agent in novel ideas, papers, and the ML landscape

Lacuna Research MCP gives AI researchers' coding agents:

Novel research proposals. Explore novel research ideas generated with Alien Science.
Research directions. Navigate concept clusters with linked papers, authors, and proposals.
Agent-ready literature. Search recent papers in markdown with source links.
Researcher intelligence. Trace authors, publications, directions, impact, and related researchers.
Landscape mapping. Compare venues, institutions, leading researchers, and publication activity.

Lacuna, built by Tiptree Systems, is a research map of machine learning: a heterogeneous knowledge graph linking papers, research directions, authors, venues, institutions, and generated research proposals, with a source trail from every derived object back to the exact paper and page that produced it. Its pipeline reconciles scholarly records from OpenAlex, OpenReview, DBLP, and arXiv; extracts concept elements from paper text and clusters them into research directions (Lacuna paper); and samples novel research proposals from those directions with Alien Science. The map spans more than 730,000 papers, 190,000+ author profiles, 38,000+ research directions, and 3,000+ research proposals built from over 15 million concept elements — and it grows continuously as new arXiv and other AI papers are ingested.

Install · First use · Tools · API reference · Configuration

Install

The easiest way to install Lacuna Research MCP is to ask your coding agent, such as Codex or Claude Code:

Install and configure the lacuna-research-mcp package from PyPI for this client.

For manual setup, the instructions below use uvx to run the latest tagged release from PyPI. Install uv first; Lacuna Research MCP requires Python 3.11 or newer.

Codex

Add the server with the Codex CLI:

bash

codex mcp add lacuna-research -- uvx lacuna-research-mcp

Alternatively, add the following to ~/.codex/config.toml (or to .codex/config.toml in a trusted project for project-only setup):

toml

[mcp_servers.lacuna-research]
command = "uvx"
args = ["lacuna-research-mcp"]

Run codex mcp list to verify the server is configured. The Codex app, CLI, and IDE extension share this configuration on the same machine.

Claude Code

Add the server for all of your projects with the Claude Code CLI:

Terminal

claude mcp add --scope user lacuna-research -- uvx lacuna-research-mcp

Omit --scope user to add it only to the current project. Alternatively, add the following under the top-level mcpServers object in ~/.claude.json:

config.json

{
  "mcpServers": {
    "lacuna-research": {
      "type": "stdio",
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Run claude mcp get lacuna-research to verify the server is configured.

Claude Desktop

Open Settings → Developer → Edit Config, then add the server under mcpServers in claude_desktop_config.json:

config.json

{
  "mcpServers": {
    "lacuna-research": {
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Restart Claude Desktop after saving the file.

Other MCP clients

For any client that supports local stdio MCP servers, use this standard configuration:

config.json

{
  "mcpServers": {
    "lacuna-research": {
      "command": "uvx",
      "args": ["lacuna-research-mcp"]
    }
  }
}

Standalone command

Install the MCP server as a persistent command:

bash

uv tool install lacuna-research-mcp
lacuna-research-mcp

Run it without installing a persistent command:

bash

uvx lacuna-research-mcp

Latest development version

PyPI contains tagged releases. To try the latest code from the main branch instead:

bash

uvx --from git+https://github.com/tiptreesystems/lacuna-research-mcp.git lacuna-research-mcp

Local development

With uv:

bash

git clone https://github.com/tiptreesystems/lacuna-research-mcp.git
cd lacuna-research-mcp
uv sync --extra dev
uv run lacuna-research-mcp

With pip:

bash

cd <path-to-lacuna-research-mcp>
python3 -m venv .venv
. .venv/bin/activate
python -m pip install -U pip
python -m pip install -e .

First use

After connecting the server, call:

search_lacuna(query="LLM jailbreak defense", search_type="hypothesis", limit=10)
search_lacuna(query="methods for detecting prompt injection attacks", search_type="papers", limit=10) (production lexical+semantic paper ranking by default)
get_hypothesis(hypothesis_id_or_url="bd35de182c2325ae")
get_paper(artifact_id_or_url="art_79c57fbfec094f26b79c422cf08fed34") (defaults to view="context")
get_direction(cluster_id_or_url=25108) (defaults to view="context")

Scope

The corpus covers machine learning and AI research: papers, research directions, authors' research output, venues, institutions, and generated research hypotheses. It does not contain biographies, news, or non-research web content. Agents should answer questions outside that scope from other sources.

What it exposes

search_lacuna Uses Lacuna's public /api/v1/search endpoint for directions, papers, authors, venues, institutions, and hypotheses. Explicit paper searches (search_type="paper") use the server's production lexical+semantic ranker when the other ranking arguments remain at their defaults. Pass search_type="hypothesis" (or "hypotheses" / "proposal" / "proposals") for hypothesis search.
get_hypothesis(hypothesis_id_or_url, view="context") Hypothesis/proposal. view="context" (default) is a compact single-fetch proposal context (summary, abstract, linked directions); view="full" returns the server's version record with version history and signal counts. Proposal bodies are in versions[].markdown.
get_direction(cluster_id_or_url, view="context") Research direction/cluster. view="context" (default) requests the compact agent-oriented summary; view="full" returns the raw cluster record.
get_direction_papers(cluster_id_or_url, page, limit, view="compact") Paginated papers attached to a direction. view="compact" (default) returns citation-ready rows (id, url, title, year, venue, a few authors, abstract snippet); view="full" returns the raw upstream paper records.
get_paper(artifact_id_or_url, view="context", figure_limit=None) Paper lookup. view="context" (default) requests the compact agent-oriented context; other views are "full", "preview", "blog", "figures", "concepts", or "neighbors". In context view, figure_limit caps the figure preview (server default 3; pass 0 to suppress previews while keeping a figures_truncated signal).
Author tools: get_author_context(…, view="context"), get_author_papers, get_author_directions, and get_author_neighbors. Start with get_author_context, which defaults to the compact agent-oriented view (capped papers plus a readable impact_directions list instead of raw impact_clusters telemetry). Use the dedicated papers, directions, and neighbors tools to page through those collections without repeating the author context. view="full" returns the server-bounded full-shape context (collections remain capped at 100). Pass include_neighbors=true to explicitly include similar authors; this may add significant server latency.
Venue and institution tools: get_venue_context(…, view="context"), get_institution_context(…, view="context"), get_institution_authors. Context tools default to compact (capped lists, duplicated blocks dropped; venue keeps a recent-activity slice that always includes the requested year). Use get_institution_authors to page through an institution's complete author list.

Wrapped APIs

MCP tool	Lacuna API endpoint
`search_lacuna(query, search_type, limit, offset, date_from, date_to, venue, sort, ranking_profile, fields)`	`GET /api/v1/search` (`fields` restricts/weights the text fields used for lexical ranking, e.g. `title^4,abstract`, and selects the experimental lexical ranker; for a default relevance-sorted paper search, this bypasses the production lexical+semantic ranker. Allowed fields are `title`, `abstract`, `summary`, `concepts`, `name`, `top_names`, `venue`, each valid only for the types that carry it — `title`: paper/cluster/venue/hypothesis; `abstract`/`summary`/`concepts`: paper; `name`: author/institution/venue; `top_names`: cluster/hypothesis; `venue`: paper/venue (`search_type="all"` spans all). Weights must satisfy `0 < weight <= 100`. Unknown fields, out-of-range weights, type-incompatible fields, and `fields` combined with `ranking_profile="semantic"` are rejected, since the server would otherwise silently drop, cap, or ignore them.)
`get_hypothesis(hypothesis_id_or_url, view="context")`	`view="context"` → `GET /api/v1/context/hypothesis/{hypothesis_id}?view=compact`; `view="full"` → `GET /api/v1/hypotheses/{hypothesis_id}`
`get_direction(cluster_id_or_url, view="context")`	`view="context"` → `GET /api/v1/context/direction/{cluster_id}?view=compact`; `view="full"` → `GET /api/v1/clusters/{cluster_id}`
`get_direction_papers(cluster_id_or_url, page, limit, view="compact")`	`GET /api/v1/clusters/{cluster_id}/papers?view=compact` (default) or `?view=complete`
`get_paper(artifact_id_or_url, view="context", figure_limit=None)`	`view="context"` → `GET /api/v1/context/paper/{artifact_id}?view=compact` (`&figure_limit=N` when set); `view="full"` → `GET /api/v1/papers/{artifact_id}`; `view="preview"` → `…/preview`; `view="blog"` → `…/blog`; `view="figures"` → `…/figures`; `view="concepts"` → `…/concepts`; `view="neighbors"` → `…/neighbors`
`get_author_papers(author_id_or_url, limit=50, offset=0)`	`GET /api/v1/authors/{author_id}/papers`
`get_author_directions(author_id_or_url, limit=50, offset=0)`	`GET /api/v1/authors/{author_id}/directions`
`get_author_context(author_id_or_url, view="context", include_neighbors=false)`	`view="context"` → `GET /api/v1/context/author/{author_id}?view=compact`; `view="full"` → `GET /api/v1/context/author/{author_id}` (`include_neighbors=true` explicitly requests similar authors)
`get_author_neighbors(author_id_or_url, limit=8, offset=0)`	`GET /api/v1/authors/{author_id}/neighbors`
`get_venue_context(venue_key_or_url, year, view="context")`	`view="context"` → `GET /api/v1/context/venue/{vkey}[/{year}]?view=compact`; `view="full"` → same route without `view`
`get_institution_context(institution_key_or_url, view="context")`	`view="context"` → `GET /api/v1/context/institution/{ikey}?view=compact`; `view="full"` → same route without `view`
`get_institution_authors(institution_key_or_url, limit=50, offset=0)`	`GET /api/v1/institutions/{ikey}/authors`

For id-or-URL arguments, pass either the raw id returned by search_lacuna or the corresponding Lacuna page URL. The MCP normalizes Lacuna-relative url and *_url fields to absolute URLs, and it also absolutifies Lacuna links inside fields named summary_markdown, article_markdown, markdown, content, or description.

get_author_context is bounded server-side in both views. The default compact view returns a curated briefing; view="full" returns the larger complete shape with embedded collections capped at 100. Use get_author_papers and get_author_directions rather than trying to page embedded context collections.

Search requests are capped at 50 results per call, and direction-paper page requests are capped at 100 results per call. In search_lacuna, date_from and date_to are inclusive publication-date bounds. Accepted formats are YYYY, YYYY-MM, and YYYY-MM-DD; for example, date_from="2020", date_to="2022-03" includes papers from January 1, 2020 through March 31, 2022.

search_lacuna exposes these ranking profiles:

default / lexical The default profile for all searches. With search_type="paper", sort="relevance", and fields unset, it uses the server's production lexical+semantic ranker with graceful fallback. The MCP's default search_type="all" uses the server's default lexical ranking instead.
semantic Use for embedding-based paper retrieval. The semantic query omits the normal lexical ranking leg, but the server can still overlay exact-title lexical matches. Only supported for paper and all searches (only papers have semantic embeddings).
bm25_title_abstract / bm25 Use for lexical matching constrained to title and abstract. Rejected for author and institution searches (those records have no title or abstract fields).

All searches use the server default unless ranking_profile is provided. The MCP rejects unsupported profile/type combinations because the server would otherwise fall back to substring search and silently ignore the requested ranking profile.

sort accepts relevance (default), year_desc, and year_asc. Year sorts cannot be combined with ranking_profile="semantic" — the server would silently ignore the sort — so the MCP rejects that combination; for recent-and-relevant queries, keep semantic ranking and constrain recency with date_from/date_to instead.

Environment variables

LACUNA_SITE_URL Defaults to https://lacuna.tiptreesystems.com
LACUNA_MCP_TIMEOUT Defaults to 30
LACUNA_MCP_MAX_RETRIES Defaults to 2; applies to timeouts, transport errors, and HTTP 429, 502, 503, and 504 responses.
LACUNA_MCP_USER_AGENT Defaults to lacuna-research-mcp/{package_version}
LACUNA_MCP_BEARER_TOKEN Optional bearer token sent as Authorization: Bearer ... for private Lacuna deployments.
LACUNA_MCP_LOG_LEVEL Defaults to WARNING (one of DEBUG, INFO, WARNING, ERROR, CRITICAL). The default keeps normal operation quiet; lower it only for debugging, since INFO/DEBUG let the HTTP client log full request URLs — including the search query string — to stderr, which some MCP hosts retain.

Environment variables are read once when the MCP server is created, or on the first direct tool/API call if the module is imported without calling create_mcp().

Implementation layout

The server is a thin MCP adapter over Lacuna's HTTP API. The implementation is split by responsibility:

lacuna_research_mcp/server.py MCPServer app creation, tool registration, lifespan cleanup, and the lacuna-research-mcp CLI entrypoint.
lacuna_research_mcp/tools.py MCP tool functions. Each tool normalizes its inputs, calls the matching Lacuna API endpoint through the shared client helpers, and returns JSON-compatible data.
lacuna_research_mcp/client.py Runtime HTTP access to Lacuna: the event-loop-bound httpx.AsyncClient, retry handling, error wrapping, JSON parsing, URL normalization on responses, and api_get/api_object/api_payload.
lacuna_research_mcp/config.py Runtime constants, RuntimeConfig, package user-agent construction, and environment parsing.
lacuna_research_mcp/ids.py Helpers that accept either raw ids or Lacuna page URLs and extract safe API path segments.
lacuna_research_mcp/normalize.py Response post-processing for relative Lacuna URLs and markdown links.
lacuna_research_mcp/errors.py User-facing exception type for Lacuna API access failures.

Notes

get_paper and get_direction default to view="context". These context views request Lacuna's compact agent-oriented payloads by default to keep MCP responses small. Paper context includes summary_markdown when available (otherwise abstract), authors, and figures; direction context includes summary_markdown, capped papers/authors/related directions, and truncation markers. Use view="full" when you need the raw metadata, and the other paper views (preview, blog, figures, concepts, neighbors) when you want one isolated sub-resource.
An explicit relevance-sorted paper search with no custom fields defaults to the server's production lexical+semantic ranker. Set ranking_profile="semantic" for embedding-based retrieval (with a possible exact-title overlay) or "bm25_title_abstract" for title-and-abstract lexical matching.
Search type aliases are normalized client-side, so papers, directions, and hypotheses are accepted and mapped to the server's singular values.
Most detail tools accept either the id returned by search or the corresponding Lacuna URL.
Relative Lacuna URLs in url/*_url response fields and fields named summary_markdown, article_markdown, markdown, content, or description are normalized to absolute URLs.
Venue and institution keys are opaque hashes (for example d7bf22905bd6), never human-readable names like icml. Find the key with search_lacuna(search_type="venue") first, or pass a /venue/... page URL.

Citation

If you find our work helpful, feel free to cite the papers behind Lacuna's research-proposal generation and research map.

Research-proposal generation — Alien Science

bibtex

@inproceedings{artiles2026alien,
  title     = {Alien Science: Sampling Coherent but Cognitively Unavailable Research Directions from Idea Atoms},
  author    = {Artiles, Alejandro H. and Weiss, Martin and Brinkmann, Levin and Goyal, Anirudh and Rahaman, Nasim},
  booktitle = {ICLR 2026 Workshop on Post-AGI Science and Society},
  year      = {2026},
  url       = {https://openreview.net/forum?id=XZWkDET1ia}
}

Research map — Lacuna

bibtex

@misc{weiss2026lacunaresearchmapmachine,
  title         = {Lacuna: A Research Map for Machine Learning},
  author        = {Martin Weiss and Miles Q. Li and Alejandro H. Artiles and Yacine Mkhinini and Chris Pal and Hugo Larochelle and Nasim Rahaman},
  year          = {2026},
  eprint        = {2606.26246},
  archivePrefix = {arXiv},
  primaryClass  = {cs.DL},
  url           = {https://arxiv.org/abs/2606.26246}
}

License

MIT. See LICENSE.